While some existing systems of fall detection are based on a wearable device (e.g., body attached accelerometers and gyroscopes), they monitor the movements of an individual by recognizing a fall and trigger an alarm. These devices, however, require an individual to wear them all the time and tend to trigger false alarms for normal daily activities. Other fall-detection systems require a person to call for help after falling down by pushing a button on a device, but such mechanism is impractical if the person becomes immobilized or unconscious after the fall.
There are significant advantages of using a non-intrusive monitoring system, such as using a video camera. A vision-based system needs to be capable of detecting various types of human behaviors for fall detection. In the literature, most of these systems rely on velocity/acceleration thresholding or single classifier to recognize falling activities. The robustness and effectiveness of these algorithms are frequently sacrificed in order to balance the trade-offs between false alarms and miss detections. Such systems, leading to a hard decision, are often resource-constraint. In many real world applications, a situation is frequently encountered that one cannot simply identify the exact behavior of an individual. For example, falling down due to unexpected reasons, lying down on the floor for rest, sitting and lying on a couch, picking up an object on the floor, sitting on the floor for exercise can always confuse even human observers. Not to mention the fact that a falling down can appear differently at different times due to different reasons.
Therefore there is a need in the art for an apparatus and method for fall detection systems which use a non-intrusive monitoring system that is able to detect exact human behaviors.